Artificial intelligence (AI) stands at the forefront of medical progress, garnering enthusiasm across a variety of specialties and levels of physician training.¹ Urology, for example, is known for its embrace of technological innovations and has been an early adopter of AI solutions to address clinical challenges; as far back as 1994, Snow et al pioneered the integration of artificial neural networks to devise diagnostic and prognostic tools for prostate cancer.² Today, medical students are well-positioned to navigate this unfolding wave of AI advancement, often referred to as the “fourth industrial revolution,” that is reshaping the landscape of health care. For these students, embracing AI is not just a choice but a necessity to stay at the forefront of medical innovation. Yet despite interest in this field,³ finding a starting point and sources of guidance in this vast area of study can be daunting. Current reports on medical education indicate a dearth of AI-focused initiatives,⁴ underscoring the need for students to take proactive steps in seeking out opportunities for learning and exploration. Participating in AI research as a medical student is a proactive way to gain early exposure to a crucial aspect of one’s future medical career. The following article provides a guide and practical starting point for students interested in exploring AI research.

The first step is to define goals. Perhaps one reason to consider AI research is to learn a new skill, such as coding, data labeling, or statistical analysis. Another compelling motivation could be to carve a professional niche centered around AI, whether within clinical practice or research pursuits. Delving deeper, it can be beneficial to contemplate the specific context in which one envisions applying AI technology. One might consider integrating it at various time points in the clinical setting (from screening to diagnosis to risk stratification) or in different urologic subspecialties (from pediatric urology to urologic oncology). Furthermore, AI can play many different roles in a physician’s career, such as optimizing scheduling, supporting decision-making, and monitoring discharged patients. With such a diverse set of uses, there are many points of entry for the aspiring researcher.

Once goals have been set, which can be adjusted as needed, the next step is to gain a basic understanding of AI. While diving into AI research is enticing—especially for busy medical students—a basic grasp of AI helps one choose research projects aligned with their interests, discover new aspects of the field, and appear well-informed when speaking with potential research mentors. Free online tutorials such as Elements of AI, University of Florida’s online AI courses, and Andrew Ng’s YouTube playlists are excellent starting points. Platforms such as Kaggle offer collaborative coding opportunities and practical AI tools. Other websites, including Towards Data Science, Medium, and KDnuggets share tutorials and case studies. For a more structured approach, consider online AI courses on Coursera (Andrew Ng), edX (Intro to AI), or Udacity. As there are numerous sources of learning, students should assess their bandwidth and choose the option that best suits their needs.

After becoming more familiar with the field, one may then reach out to principal investigators for potential opportunities. Students should start by checking within their own medical school for available positions. Many universities have AI research initiatives within medical or engineering schools, affiliated hospitals, or health care institutions; some even offer specific research programs or fellowships focused on AI in medicine. Establishing connections, both online and in person, is a crucial aspect of discovering and securing AI research opportunities. Departmental advisors, mentors, or faculty can guide students toward ongoing projects. Online platforms, such as university research portals, medical school bulletin boards, ResearchGate, Twitter, or LinkedIn, are often used by principal investigators to share research opportunities. Networking through professional organizations and attending conferences can also be fruitful. Students should tailor their approach based on the specific resources available to them and the nature of AI research in their field of interest.

AI research tasks conducted by medical students can vary. Students with programming experience may directly work with algorithms. However, even those with more limited coding expertise can play a pivotal role—tasks such as labeling data to meet specific criteria, such as identifying features in images or classifying phrases in text, provide valuable assistance in training AI models. Additionally, students can enhance datasets by identifying errors, introducing more nuanced labels, or validating and cross-referencing annotations. Other potential contributions include collecting raw data from electronic health records, testing existing AI algorithms, or even generating new questions or ideas. The versatility of AI research allows for diverse and impactful student involvement.

There are also ample avenues to get involved in AI beyond the lab. Submitting to both urology and AI conferences can garner well-rounded feedback. Some conferences might have overlapping themes—for example, the AUA recently introduced the new abstract category “Artificial Intelligence.” Some medical schools, such as the University of Florida, even have an “AI pathway” which allows medical students to take AI coursework early in their career.⁵ Student groups are another possibility—the University of Washington School of Medicine’s Medical Student Technology Advisory Team⁶ supports technology and AI-related projects by and for medical students. Some institutions hold AI-centered health conferences,⁷ and many universities have engineering schools where students can collaborate on AI-related projects. There are myriad opportunities beyond research to get involved as a medical student.

In the ever-expanding realm of medicine, where the significance of AI continues to rise, it is crucial for emerging physicians to be adequately equipped to delve into and make meaningful contributions to this transformative field. This article serves as a practical guide, providing medical students with insights into setting goals, gaining basic AI knowledge, securing research opportunities, and getting more involved in AI opportunities. As they embark on this journey, armed with a proactive approach to conducting research, medical students are not just observers but active participants in shaping the trajectory of AI’s impact on the future of medicine.